update configs

* use directory for model overrides instead

* fix maxEpisodes setting

.gitignore

 
 # Python virtual environment
 venv/
+venv1_0/
 .mypy_cache/
 
 # Code coverage report

Project/Assets/ML-Agents/Examples/WallJump/Materials/TransparentWall.mat

 Material:
   serializedVersion: 6
   m_ObjectHideFlags: 0
-  m_PrefabParentObject: {fileID: 0}
-  m_PrefabInternal: {fileID: 0}
+  m_CorrespondingSourceObject: {fileID: 0}
+  m_PrefabInstance: {fileID: 0}
+  m_PrefabAsset: {fileID: 0}
   m_Name: TransparentWall
   m_Shader: {fileID: 46, guid: 0000000000000000f000000000000000, type: 0}
   m_ShaderKeywords: _ALPHABLEND_ON

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs

     public class ProjectSettingsOverrides : MonoBehaviour
     {
         // Original values
-
+        bool m_OriginalUseEnhancedDeterminism;
+        bool m_OriginalReuseCollisionCallbacks;
 
         [Tooltip("Increase or decrease the scene gravity. Use ~3x to make things less floaty")]
         public float gravityMultiplier = 1.0f;
         public int solverIterations = 6;
         [Tooltip("Affects how accurately the Rigidbody joints and collision contacts are resolved. (default 1). Must be positive.")]
         public int solverVelocityIterations = 1;
+        [Tooltip("Determines whether the garbage collector should reuse only a single instance of a Collision type for all collision callbacks. Reduces Garbage.")]
+        public bool reuseCollisionCallbacks = true;
 
         public void Awake()
         {
             m_OriginalMaximumDeltaTime = Time.maximumDeltaTime;
             m_OriginalSolverIterations = Physics.defaultSolverIterations;
             m_OriginalSolverVelocityIterations = Physics.defaultSolverVelocityIterations;
+            m_OriginalReuseCollisionCallbacks = Physics.reuseCollisionCallbacks ;
 
             // Override
             Physics.gravity *= gravityMultiplier;
             Physics.defaultSolverVelocityIterations = solverVelocityIterations;
+            Physics.reuseCollisionCallbacks = reuseCollisionCallbacks;
 
             // Make sure the Academy singleton is initialized first, since it will create the SideChannels.
             Academy.Instance.EnvironmentParameters.RegisterCallback("gravity", f => { Physics.gravity = new Vector3(0, -f, 0); });
             Time.maximumDeltaTime = m_OriginalMaximumDeltaTime;
             Physics.defaultSolverIterations = m_OriginalSolverIterations;
             Physics.defaultSolverVelocityIterations = m_OriginalSolverVelocityIterations;
+            Physics.reuseCollisionCallbacks = m_OriginalReuseCollisionCallbacks;
         }
     }
 }

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs

                 startingPos = t.position,
                 startingRot = t.rotation
             };
-            bp.rb.maxAngularVelocity = 100;
+            bp.rb.maxAngularVelocity = 50;
 
             // Add & setup the ground contact script
             bp.groundContact = t.GetComponent<GroundContact>();

Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs

+using MLAgentsExamples;
+using UnityEditor;
+using BodyPart = Unity.MLAgentsExamples.BodyPart;
+    [Header("Walking Speed")]
+    [Space(10)]
+    public float maximumWalkingSpeed = 999; //The max walk velocity magnitude an agent will be rewarded for
+    Vector3 m_WalkDir;
+    Quaternion m_WalkDirLookRot;
+    
+    [Space(10)]
+    [Header("Orientation Cube")]
+    [Space(10)]
+    //This will be used as a stable observation platform for the ragdoll to use.
+    GameObject m_OrientationCube;
+    
+    public float targetSpawnRadius;
+    public Transform ground;
+    public bool detectTargets;
+    public bool targetIsStatic;
+    public bool respawnTargetWhenTouched;
+    
-    Vector3 m_DirToTarget;
+    [Header("Body Parts")]
+    [Space(10)]
     public Transform hips;
     public Transform chest;
     public Transform spine;
     Rigidbody m_SpineRb;
 
     EnvironmentParameters m_ResetParams;
+    
+
+        //Spawn an orientation cube
+        Vector3 oCubePos = hips.position;
+        oCubePos.y = -.45f;
+        m_OrientationCube = Instantiate(Resources.Load<GameObject>("OrientationCube"), oCubePos, Quaternion.identity);
+        m_OrientationCube.transform.SetParent(transform);
+        
+        UpdateOrientationCube();
+
         m_JdController = GetComponent<JointDriveController>();
         m_JdController.SetupBodyPart(hips);
         m_JdController.SetupBodyPart(chest);
     /// </summary>
     public void CollectObservationBodyPart(BodyPart bp, VectorSensor sensor)
     {
-        var rb = bp.rb;
+        //GROUND CHECK
-        sensor.AddObservation(rb.velocity);
-        sensor.AddObservation(rb.angularVelocity);
-        var localPosRelToHips = hips.InverseTransformPoint(rb.position);
-        sensor.AddObservation(localPosRelToHips);
+        
+        //Get velocities in the context of our orientation cube's space
+        //Note: You can get these velocities in world space as well but it may not train as well.
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.velocity));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.angularVelocity));
+        
+        //Get position relative to hips in the context of our orientation cube's space
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.position - hips.position));
-        if (bp.rb.transform != hips && bp.rb.transform != handL && bp.rb.transform != handR &&
-            bp.rb.transform != footL && bp.rb.transform != footR && bp.rb.transform != head)
+        if (bp.rb.transform != hips && bp.rb.transform != handL && bp.rb.transform != handR)
-            sensor.AddObservation(bp.currentXNormalizedRot);
-            sensor.AddObservation(bp.currentYNormalizedRot);
-            sensor.AddObservation(bp.currentZNormalizedRot);
+            sensor.AddObservation(bp.rb.transform.localRotation);
-
+    
-        m_JdController.GetCurrentJointForces();
+        sensor.AddObservation(Quaternion.FromToRotation(hips.forward, m_OrientationCube.transform.forward));
+        sensor.AddObservation(Quaternion.FromToRotation(head.forward, m_OrientationCube.transform.forward));
+        
+        //clamp the distance vector in case the target is far away. normalized to 1.
+//        var clampedDistFromCubeToTarget = (Vector3.ClampMagnitude(m_OrientationCube.transform.InverseTransformPoint(target.position), 15))/15;
+//        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(clampedDistFromCubeToTarget));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(target.position));
-        sensor.AddObservation(m_DirToTarget.normalized);
-        sensor.AddObservation(m_JdController.bodyPartsDict[hips].rb.position);
-        sensor.AddObservation(hips.forward);
-        sensor.AddObservation(hips.up);
+//        print(m_OrientationCube.transform.InverseTransformPoint(target.position));
+//        sensor.AddObservation(target.position - m_OrientationCube.transform.position);
-        foreach (var bodyPart in m_JdController.bodyPartsDict.Values)
+        foreach (var bodyPart in m_JdController.bodyPartsList)
         {
             CollectObservationBodyPart(bodyPart, sensor);
         }
         bpDict[shinR].SetJointTargetRotation(vectorAction[++i], 0, 0);
         bpDict[footR].SetJointTargetRotation(vectorAction[++i], vectorAction[++i], vectorAction[++i]);
         bpDict[footL].SetJointTargetRotation(vectorAction[++i], vectorAction[++i], vectorAction[++i]);
-
 
         bpDict[armL].SetJointTargetRotation(vectorAction[++i], vectorAction[++i], 0);
         bpDict[armR].SetJointTargetRotation(vectorAction[++i], vectorAction[++i], 0);
         bpDict[forearmR].SetJointStrength(vectorAction[++i]);
     }
 
+    void UpdateOrientationCube()
+    {
+        //FACING DIR
+        m_WalkDir = target.position - m_OrientationCube.transform.position;
+        m_WalkDir.y = 0; //flatten dir on the y
+        m_WalkDirLookRot = Quaternion.LookRotation(m_WalkDir); //get our look rot to the target
+        
+        //UPDATE ORIENTATION CUBE POS & ROT
+        m_OrientationCube.transform.position = hips.position;
+        m_OrientationCube.transform.rotation = m_WalkDirLookRot;
+    }
+    
+    public int fuTimer = 0;
+    public float fudeltatime = 0;
+    public float deltatime = 0;
+
+    void Update()
+    {
+        deltatime = Time.deltaTime;
+    }
+
+        fuTimer++;
+        fudeltatime = Time.fixedDeltaTime;
+        if (detectTargets)
+        {
+            foreach (var bodyPart in m_JdController.bodyPartsDict.Values)
+            {
+                if (bodyPart.targetContact && bodyPart.targetContact.touchingTarget)
+                {
+                    TouchedTarget();
+                }
+            }
+        }
+        
+        UpdateOrientationCube();
+        
-        // d. Discourage head movement.
-        m_DirToTarget = target.position - m_JdController.bodyPartsDict[hips].rb.position;
-            +0.03f * Vector3.Dot(m_DirToTarget.normalized, m_JdController.bodyPartsDict[hips].rb.velocity)
-            + 0.01f * Vector3.Dot(m_DirToTarget.normalized, hips.forward)
-            + 0.02f * (head.position.y - hips.position.y)
-            - 0.01f * Vector3.Distance(m_JdController.bodyPartsDict[head].rb.velocity,
-                m_JdController.bodyPartsDict[hips].rb.velocity)
+            +0.02f * Vector3.Dot(m_OrientationCube.transform.forward,
+                Vector3.ClampMagnitude(m_JdController.bodyPartsDict[hips].rb.velocity, maximumWalkingSpeed))
+            + 0.01f * Vector3.Dot(m_OrientationCube.transform.forward, head.forward)
+            + 0.005f * (head.position.y - footL.position.y)
+            + 0.005f * (head.position.y - footR.position.y)
-    /// Loop over body parts and reset them to initial conditions.
+    /// Agent touched the target
-    public override void OnEpisodeBegin()
+    public void TouchedTarget()
-        if (m_DirToTarget != Vector3.zero)
+        AddReward(1f);
+        if (respawnTargetWhenTouched)
-            transform.rotation = Quaternion.LookRotation(m_DirToTarget);
+            GetRandomTargetPos();
+    }
+    /// <summary>
+    /// Moves target to a random position within specified radius.
+    /// </summary>
+    public void GetRandomTargetPos()
+    {
+        var newTargetPos = Random.insideUnitSphere * targetSpawnRadius;
+        newTargetPos.y = 5;
+        target.position = newTargetPos + ground.position;
+    }
+
+    /// <summary>
+    /// Loop over body parts and reset them to initial conditions.
+    /// </summary>
+    public override void OnEpisodeBegin()
+    {
+        
+        //Random start rotation
+        transform.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+        
+        UpdateOrientationCube();
+
+        if (detectTargets && !targetIsStatic)
+        {
+            GetRandomTargetPos();
+        }
+
         SetResetParameters();
     }
 
     public void SetResetParameters()
     {
         SetTorsoMass();
+    }
+
+    private void OnDrawGizmosSelected()
+    {
+        if (Application.isPlaying)
+        {   
+            Gizmos.color = Color.green;
+            Gizmos.matrix = m_OrientationCube.transform.localToWorldMatrix;
+            Gizmos.DrawWireCube(Vector3.zero, m_OrientationCube.transform.localScale);
+            Gizmos.DrawRay(Vector3.zero, Vector3.forward);
+        }
     }
 }

Project/ProjectSettings/DynamicsManager.asset

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   m_AutoSimulation: 1
   m_AutoSyncTransforms: 1
-  m_ReuseCollisionCallbacks: 0
+  m_ReuseCollisionCallbacks: 1
   m_ClothInterCollisionSettingsToggle: 0
   m_ContactPairsMode: 0
   m_BroadphaseType: 0

Project/ProjectSettings/ProjectVersion.txt

-m_EditorVersion: 2018.4.17f1
+m_EditorVersion: 2018.4.18f1

config/ppo/WalkerStatic.yaml

 behaviors:
-  Walker:
+  WalkerStatic:
     trainer: ppo
     batch_size: 2048
     beta: 0.005

Project/Assets/ExpertWalkerDyn.demo.meta

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Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs

+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    public class DirectionIndicator : MonoBehaviour
+    {
+        public Transform transformToFollow; //ex: hips or body
+        public Transform targetToLookAt; //target in the scene the indicator will point to
+        public float heightOffset;
+        private Vector3 m_StartingPos;
+        
+        void OnEnable()
+        {
+            m_StartingPos = transform.position;
+        }
+
+        void Update()
+        {
+            transform.position = new Vector3(transformToFollow.position.x, m_StartingPos.y + heightOffset, transformToFollow.position.z);
+            Vector3 m_WalkDir = targetToLookAt.position - transform.position;
+            m_WalkDir.y = 0; //flatten dir on the y
+            transform.rotation = Quaternion.LookRotation(m_WalkDir);
+        }
+    }
+}

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config/trainer_config.yaml

+default:
+    trainer: ppo
+    batch_size: 1024
+    beta: 5.0e-3
+    buffer_size: 10240
+    epsilon: 0.2
+    hidden_units: 128
+    lambd: 0.95
+    learning_rate: 3.0e-4
+    learning_rate_schedule: linear
+    max_steps: 5.0e5
+    memory_size: 128
+    normalize: false
+    num_epoch: 3
+    num_layers: 2
+    time_horizon: 64
+    sequence_length: 64
+    summary_freq: 10000
+    use_recurrent: false
+    vis_encode_type: simple
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.99
+
+FoodCollector:
+    normalize: false
+    beta: 5.0e-3
+    batch_size: 1024
+    buffer_size: 10240
+    max_steps: 2.0e6
+
+Bouncer:
+    normalize: true
+    max_steps: 4.0e6
+    num_layers: 2
+    hidden_units: 64
+
+PushBlock:
+    max_steps: 2.0e6
+    batch_size: 128
+    buffer_size: 2048
+    beta: 1.0e-2
+    hidden_units: 256
+    summary_freq: 60000
+    time_horizon: 64
+    num_layers: 2
+
+SmallWallJump:
+    max_steps: 5e6
+    batch_size: 128
+    buffer_size: 2048
+    beta: 5.0e-3
+    hidden_units: 256
+    summary_freq: 20000
+    time_horizon: 128
+    num_layers: 2
+    normalize: false
+
+BigWallJump:
+    max_steps: 2e7
+    batch_size: 128
+    buffer_size: 2048
+    beta: 5.0e-3
+    hidden_units: 256
+    summary_freq: 20000
+    time_horizon: 128
+    num_layers: 2
+    normalize: false
+
+Pyramids:
+    summary_freq: 30000
+    time_horizon: 128
+    batch_size: 128
+    buffer_size: 2048
+    hidden_units: 512
+    num_layers: 2
+    beta: 1.0e-2
+    max_steps: 1.0e7
+    num_epoch: 3
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.99
+        curiosity:
+            strength: 0.02
+            gamma: 0.99
+            encoding_size: 256
+
+VisualPyramids:
+    time_horizon: 128
+    batch_size: 64
+    buffer_size: 2024
+    hidden_units: 256
+    num_layers: 1
+    beta: 1.0e-2
+    max_steps: 1.0e7
+    num_epoch: 3
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.99
+        curiosity:
+            strength: 0.01
+            gamma: 0.99
+            encoding_size: 256
+
+3DBall:
+    normalize: true
+    batch_size: 64
+    buffer_size: 12000
+    summary_freq: 12000
+    time_horizon: 1000
+    lambd: 0.99
+    beta: 0.001
+
+3DBallHard:
+    normalize: true
+    batch_size: 1200
+    buffer_size: 12000
+    summary_freq: 12000
+    time_horizon: 1000
+    max_steps: 5.0e6
+    beta: 0.001
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+Tennis:
+    normalize: true
+    max_steps: 5.0e7
+    learning_rate_schedule: constant
+    batch_size: 1024
+    buffer_size: 10240
+    hidden_units: 256
+    time_horizon: 1000
+    self_play:
+        window: 10
+        play_against_latest_model_ratio: 0.5
+        save_steps: 50000
+        swap_steps: 50000
+        team_change: 100000
+
+Soccer:
+    normalize: false
+    max_steps: 5.0e7
+    learning_rate_schedule: constant
+    batch_size: 2048
+    buffer_size: 20480
+    hidden_units: 512
+    time_horizon: 1000
+    num_layers: 2
+    self_play:
+        window: 10
+        play_against_latest_model_ratio: 0.5
+        save_steps: 50000
+        swap_steps: 50000
+        team_change: 100000
+
+CrawlerStatic:
+    normalize: true
+    num_epoch: 3
+    time_horizon: 1000
+    batch_size: 2024
+    buffer_size: 20240
+    max_steps: 1e7
+    summary_freq: 30000
+    num_layers: 3
+    hidden_units: 512
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+CrawlerDynamic:
+    normalize: true
+    num_epoch: 3
+    time_horizon: 1000
+    batch_size: 2024
+    buffer_size: 20240
+    max_steps: 3.5e6
+    # max_steps: 1e7
+    summary_freq: 30000
+    num_layers: 3
+    hidden_units: 512
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+WormDynamic:
+    normalize: true
+    num_epoch: 3
+    time_horizon: 1000
+    batch_size: 2024
+    buffer_size: 20240
+    max_steps: 3.5e6
+    summary_freq: 30000
+    num_layers: 3
+    hidden_units: 512
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+WormStatic:
+    normalize: true
+    num_epoch: 3
+    time_horizon: 1000
+    batch_size: 2024
+    buffer_size: 20240
+    max_steps: 3.5e6
+    summary_freq: 30000
+    num_layers: 3
+    hidden_units: 512
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+WalkerDynamic:
+    normalize: true
+    # learning_rate_schedule: constant
+    num_epoch: 10
+    time_horizon: 1000
+    batch_size: 2048
+    buffer_size: 20480
+    # batch_size: 24576 #2048
+    # buffer_size: 245760 #20480
+    max_steps: 2e7
+    beta: 9.0e-3
+    epsilon: 0.3
+    # max_steps: 1e8
+    summary_freq: 30000
+    # learning_rate: 1.0e-3
+    num_layers: 3
+    hidden_units: 512
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+# WalkerDynamic:
+#     normalize: true
+#     num_epoch: 3
+#     time_horizon: 1000
+#     batch_size: 2048
+#     buffer_size: 20480
+#     max_steps: 2e7
+#     summary_freq: 30000
+#     num_layers: 3
+#     hidden_units: 512
+#     reward_signals:
+#         extrinsic:
+#             strength: 1.0
+#             gamma: 0.995
+
+WalkerStatic:
+    normalize: true
+    num_epoch: 3
+    time_horizon: 1000
+    batch_size: 2048
+    buffer_size: 20480
+    max_steps: 2e7
+    summary_freq: 30000
+    num_layers: 3
+    hidden_units: 512
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+Reacher:
+    normalize: true
+    num_epoch: 3
+    time_horizon: 1000
+    batch_size: 2024
+    buffer_size: 20240
+    max_steps: 2e7
+    summary_freq: 60000
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.995
+
+Hallway:
+    use_recurrent: true
+    sequence_length: 64
+    num_layers: 2
+    hidden_units: 128
+    memory_size: 128
+    beta: 1.0e-2
+    num_epoch: 3
+    buffer_size: 1024
+    batch_size: 128
+    max_steps: 1.0e7
+    summary_freq: 10000
+    time_horizon: 64
+
+VisualHallway:
+    use_recurrent: true
+    sequence_length: 64
+    num_layers: 1
+    hidden_units: 128
+    memory_size: 128
+    beta: 1.0e-2
+    num_epoch: 3
+    buffer_size: 1024
+    batch_size: 64
+    max_steps: 1.0e7
+    summary_freq: 10000
+    time_horizon: 64
+
+VisualPushBlock:
+    use_recurrent: true
+    sequence_length: 32
+    num_layers: 1
+    hidden_units: 128
+    memory_size: 128
+    beta: 1.0e-2
+    num_epoch: 3
+    buffer_size: 1024
+    batch_size: 64
+    max_steps: 3.0e6
+    summary_freq: 60000
+    time_horizon: 64
+
+GridWorld:
+    batch_size: 32
+    normalize: false
+    num_layers: 1
+    hidden_units: 256
+    beta: 5.0e-3
+    buffer_size: 256
+    max_steps: 500000
+    summary_freq: 20000
+    time_horizon: 5
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.9
+
+Basic:
+    batch_size: 32
+    normalize: false
+    num_layers: 1
+    hidden_units: 20
+    beta: 5.0e-3
+    buffer_size: 256
+    max_steps: 5.0e5
+    summary_freq: 2000
+    time_horizon: 3
+    reward_signals:
+        extrinsic:
+            strength: 1.0
+            gamma: 0.9

config/ppo/WalkerDynamic.yaml

+behaviors:
+  WalkerDynamic:
+    trainer: ppo
+    batch_size: 2048
+    beta: 0.005
+    buffer_size: 20480
+    epsilon: 0.2
+    hidden_units: 512
+    lambd: 0.95
+    learning_rate: 0.0003
+    learning_rate_schedule: linear
+    max_steps: 2e7
+    memory_size: 128
+    normalize: true
+    num_epoch: 3
+    num_layers: 3
+    time_horizon: 1000
+    sequence_length: 64
+    summary_freq: 30000
+    use_recurrent: false
+    vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        strength: 1.0
+        gamma: 0.995

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